使用卷积神经网络对混沌信号进行解调
Demodulation of chaotic signals using convolutional neural network
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中文总结 AI 辅助
研究针对混沌信号解调问题，采用基于深度学习的方法，通过卷积神经网络架构对混沌逻辑映射生成信号进行解调，在特定噪声和参数条件下得出误码率，展现出能检测未在训练集的混沌模式的能力。
中文摘要 AI 辅助
混沌调制是一种利用确定性混沌产生伪随机信号的有效通信技术，常用方法是调制混沌分岔参数。本文介绍了一种基于深度学习的分岔参数键控解调方法，描述了卷积神经网络架构，并评估了使用混沌逻辑映射生成信号的性能指标。研究了二进制信号的误码率，在信噪比为-13dB（对应归一化信噪比为+20dB）的加性高斯白噪声下，分岔参数偏差为1.34%时误码率为0.0819。结果表明即使训练数据集中未包含特定模式，该方法也能检测混沌模式。
英文摘要
Chaotic modulation is an effective communication technique that exploits deterministic chaos to produce pseudo-random signals. A widely adopted approach involves modulation of the chaotic bifurcation parameter. This paper introduces a deep learning-based demodulation method for keying of the bifurcation parameter. It describes the architecture of the convolutional neural network and evaluates performance metrics for signals generated using the chaotic logistic map. The study assesses the bit error rate for binary signals and reports a bit error rate of 0.0819 for a bifurcation parameter deviation of 1.34% under additive white Gaussian noise at a signal-to-noise ratio of -13 dB (corresponding to a normalized signal-to-noise ratio of +20 dB). The results demonstrate the capability to detect chaotic patterns even when the specific patterns were not included in the training dataset.
发表机构
- SoftServe Inc（软服务公司）
- Karabuk University（卡拉比克大学）
- Universitas Ahmad Dahlan（艾哈迈德·达兰大学）
机构由 AI 辅助整理，请以论文原文为准。
